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       <span id="trtorch">
       </span>
       <span id="trtorch-py">
       </span>
       <h1 id="py-api-trtorch--page-root">
        trtorch
        <a class="headerlink" href="#py-api-trtorch--page-root" title="Permalink to this headline">
         ¶
        </a>
       </h1>
       <h2 id="functions">
        Functions
        <a class="headerlink" href="#functions" title="Permalink to this headline">
         ¶
        </a>
       </h2>
       <dl class="py function">
        <dt id="trtorch.compile">
         <code class="sig-prename descclassname">
          trtorch.
         </code>
         <code class="sig-name descname">
          compile
         </code>
         <span class="sig-paren">
          (
         </span>
         <em class="sig-param">
          <span class="n">
           module
          </span>
          <span class="p">
           :
          </span>
          <span class="n">
           torch.jit.ScriptModule
          </span>
         </em>
         ,
         <em class="sig-param">
          <span class="n">
           extra_info
          </span>
          <span class="p">
           :
          </span>
          <span class="n">
           Any
          </span>
         </em>
         <span class="sig-paren">
          )
         </span>
         → torch.jit.ScriptModule
         <a class="headerlink" href="#trtorch.compile" title="Permalink to this definition">
          ¶
         </a>
        </dt>
        <dd>
         <p>
          Compile a TorchScript module for NVIDIA GPUs using TensorRT
         </p>
         <p>
          Takes a existing TorchScript module and a set of settings to configure the compiler
and will convert methods to JIT Graphs which call equivalent TensorRT engines
         </p>
         <p>
          Converts specifically the forward method of a TorchScript Module
         </p>
         <dl class="field-list simple">
          <dt class="field-odd">
           Parameters
          </dt>
          <dd class="field-odd">
           <ul class="simple">
            <li>
             <p>
              <strong>
               module
              </strong>
              (
              <em>
               torch.jit.ScriptModule
              </em>
              ) – Source module, a result of tracing or scripting a PyTorch
              <code class="docutils literal notranslate">
               <span class="pre">
                torch.nn.Module
               </span>
              </code>
             </p>
            </li>
            <li>
             <p>
              <strong>
               extra_info
              </strong>
              (
              <em>
               dict
              </em>
              ) –
             </p>
             <p>
              Compilation settings including operating precision, target device, etc.
One key is required which is
              <code class="docutils literal notranslate">
               <span class="pre">
                input_shapes
               </span>
              </code>
              , describing the input sizes or ranges for inputs
to the graph. All other keys are optional
             </p>
             <div class="highlight-py notranslate">
              <div class="highlight">
               <pre><span></span><span class="n">ExtraInfo</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s2">"input_shapes"</span><span class="p">:</span> <span class="p">[</span>
        <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">),</span> <span class="c1"># Static input shape for input #1</span>
        <span class="p">{</span>
            <span class="s2">"min"</span><span class="p">:</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">),</span>
            <span class="s2">"opt"</span><span class="p">:</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">),</span>
            <span class="s2">"max"</span><span class="p">:</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1024</span><span class="p">,</span> <span class="mi">1024</span><span class="p">)</span>
        <span class="p">}</span> <span class="c1"># Dynamic input shape for input #2</span>
    <span class="p">],</span>
    <span class="s2">"op_precision"</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">half</span><span class="p">,</span> <span class="c1"># Operating precision set to FP16</span>
    <span class="s2">"refit"</span><span class="p">:</span> <span class="n">false</span><span class="p">,</span> <span class="c1"># enable refit</span>
    <span class="s2">"debug"</span><span class="p">:</span> <span class="n">false</span><span class="p">,</span> <span class="c1"># enable debuggable engine</span>
    <span class="s2">"strict_types"</span><span class="p">:</span> <span class="n">false</span><span class="p">,</span> <span class="c1"># kernels should strictly run in operating precision</span>
    <span class="s2">"allow_gpu_fallback"</span><span class="p">:</span> <span class="n">false</span><span class="p">,</span> <span class="c1"># (DLA only) Allow layers unsupported on DLA to run on GPU</span>
    <span class="s2">"device"</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">"cuda"</span><span class="p">),</span> <span class="c1"># Type of device to run engine on (for DLA use trtorch.DeviceType.DLA)</span>
    <span class="s2">"capability"</span><span class="p">:</span> <span class="n">trtorch</span><span class="o">.</span><span class="n">EngineCapability</span><span class="o">.</span><span class="n">DEFAULT</span><span class="p">,</span> <span class="c1"># Restrict kernel selection to safe gpu kernels or safe dla kernels</span>
    <span class="s2">"num_min_timing_iters"</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span> <span class="c1"># Number of minimization timing iterations used to select kernels</span>
    <span class="s2">"num_avg_timing_iters"</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="c1"># Number of averaging timing iterations used to select kernels</span>
    <span class="s2">"workspace_size"</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="c1"># Maximum size of workspace given to TensorRT</span>
    <span class="s2">"max_batch_size"</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="c1"># Maximum batch size (must be &gt;= 1 to be set, 0 means not set)</span>
<span class="p">}</span>
</pre>
              </div>
             </div>
             <p>
              Input Sizes can be specified as torch sizes, tuples or lists. Op precisions can be specified using
torch datatypes or trtorch datatypes and you can use either torch devices or the trtorch device type enum
to select device type.
             </p>
            </li>
           </ul>
          </dd>
          <dt class="field-even">
           Returns
          </dt>
          <dd class="field-even">
           <p>
            Compiled TorchScript Module, when run it will execute via TensorRT
           </p>
          </dd>
          <dt class="field-odd">
           Return type
          </dt>
          <dd class="field-odd">
           <p>
            torch.jit.ScriptModule
           </p>
          </dd>
         </dl>
        </dd>
       </dl>
       <dl class="py function">
        <dt id="trtorch.convert_method_to_trt_engine">
         <code class="sig-prename descclassname">
          trtorch.
         </code>
         <code class="sig-name descname">
          convert_method_to_trt_engine
         </code>
         <span class="sig-paren">
          (
         </span>
         <em class="sig-param">
          <span class="n">
           module
          </span>
          <span class="p">
           :
          </span>
          <span class="n">
           torch.jit.ScriptModule
          </span>
         </em>
         ,
         <em class="sig-param">
          <span class="n">
           method_name
          </span>
          <span class="p">
           :
          </span>
          <span class="n">
           str
          </span>
         </em>
         ,
         <em class="sig-param">
          <span class="n">
           extra_info
          </span>
          <span class="p">
           :
          </span>
          <span class="n">
           Any
          </span>
         </em>
         <span class="sig-paren">
          )
         </span>
         → str
         <a class="headerlink" href="#trtorch.convert_method_to_trt_engine" title="Permalink to this definition">
          ¶
         </a>
        </dt>
        <dd>
         <p>
          Convert a TorchScript module method to a serialized TensorRT engine
         </p>
         <p>
          Converts a specified method of a module to a serialized TensorRT engine given a dictionary of conversion settings
         </p>
         <dl class="field-list simple">
          <dt class="field-odd">
           Parameters
          </dt>
          <dd class="field-odd">
           <ul class="simple">
            <li>
             <p>
              <strong>
               module
              </strong>
              (
              <em>
               torch.jit.ScriptModule
              </em>
              ) – Source module, a result of tracing or scripting a PyTorch
              <code class="docutils literal notranslate">
               <span class="pre">
                torch.nn.Module
               </span>
              </code>
             </p>
            </li>
            <li>
             <p>
              <strong>
               method_name
              </strong>
              (
              <em>
               str
              </em>
              ) – Name of method to convert
             </p>
            </li>
            <li>
             <p>
              <strong>
               extra_info
              </strong>
              (
              <em>
               dict
              </em>
              ) –
             </p>
             <p>
              Compilation settings including operating precision, target device, etc.
One key is required which is
              <code class="docutils literal notranslate">
               <span class="pre">
                input_shapes
               </span>
              </code>
              , describing the input sizes or ranges for inputs
to the graph. All other keys are optional
             </p>
             <div class="highlight-py notranslate">
              <div class="highlight">
               <pre><span></span><span class="n">ExtraInfo</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s2">"input_shapes"</span><span class="p">:</span> <span class="p">[</span>
        <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">),</span> <span class="c1"># Static input shape for input #1</span>
        <span class="p">{</span>
            <span class="s2">"min"</span><span class="p">:</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">),</span>
            <span class="s2">"opt"</span><span class="p">:</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">),</span>
            <span class="s2">"max"</span><span class="p">:</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1024</span><span class="p">,</span> <span class="mi">1024</span><span class="p">)</span>
        <span class="p">}</span> <span class="c1"># Dynamic input shape for input #2</span>
    <span class="p">],</span>
    <span class="s2">"op_precision"</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">half</span><span class="p">,</span> <span class="c1"># Operating precision set to FP16</span>
    <span class="s2">"refit"</span><span class="p">:</span> <span class="n">false</span><span class="p">,</span> <span class="c1"># enable refit</span>
    <span class="s2">"debug"</span><span class="p">:</span> <span class="n">false</span><span class="p">,</span> <span class="c1"># enable debuggable engine</span>
    <span class="s2">"strict_types"</span><span class="p">:</span> <span class="n">false</span><span class="p">,</span> <span class="c1"># kernels should strictly run in operating precision</span>
    <span class="s2">"allow_gpu_fallback"</span><span class="p">:</span> <span class="n">false</span><span class="p">,</span> <span class="c1"># (DLA only) Allow layers unsupported on DLA to run on GPU</span>
    <span class="s2">"device"</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">"cuda"</span><span class="p">),</span> <span class="c1"># Type of device to run engine on (for DLA use trtorch.DeviceType.DLA)</span>
    <span class="s2">"capability"</span><span class="p">:</span> <span class="n">trtorch</span><span class="o">.</span><span class="n">EngineCapability</span><span class="o">.</span><span class="n">DEFAULT</span><span class="p">,</span> <span class="c1"># Restrict kernel selection to safe gpu kernels or safe dla kernels</span>
    <span class="s2">"num_min_timing_iters"</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span> <span class="c1"># Number of minimization timing iterations used to select kernels</span>
    <span class="s2">"num_avg_timing_iters"</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="c1"># Number of averaging timing iterations used to select kernels</span>
    <span class="s2">"workspace_size"</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="c1"># Maximum size of workspace given to TensorRT</span>
    <span class="s2">"max_batch_size"</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="c1"># Maximum batch size (must be &gt;= 1 to be set, 0 means not set)</span>
<span class="p">}</span>
</pre>
              </div>
             </div>
             <p>
              Input Sizes can be specified as torch sizes, tuples or lists. Op precisions can be specified using
torch datatypes or trtorch datatypes and you can use either torch devices or the trtorch device type enum
to select device type.
             </p>
            </li>
           </ul>
          </dd>
          <dt class="field-even">
           Returns
          </dt>
          <dd class="field-even">
           <p>
            Serialized TensorRT engine, can either be saved to a file or deserialized via TensorRT APIs
           </p>
          </dd>
          <dt class="field-odd">
           Return type
          </dt>
          <dd class="field-odd">
           <p>
            bytes
           </p>
          </dd>
         </dl>
        </dd>
       </dl>
       <dl class="py function">
        <dt id="trtorch.check_method_op_support">
         <code class="sig-prename descclassname">
          trtorch.
         </code>
         <code class="sig-name descname">
          check_method_op_support
         </code>
         <span class="sig-paren">
          (
         </span>
         <em class="sig-param">
          <span class="n">
           module
          </span>
          <span class="p">
           :
          </span>
          <span class="n">
           torch.jit.ScriptModule
          </span>
         </em>
         ,
         <em class="sig-param">
          <span class="n">
           method_name
          </span>
          <span class="p">
           :
          </span>
          <span class="n">
           str
          </span>
         </em>
         <span class="sig-paren">
          )
         </span>
         → bool
         <a class="headerlink" href="#trtorch.check_method_op_support" title="Permalink to this definition">
          ¶
         </a>
        </dt>
        <dd>
         <p>
          Checks to see if a method is fully supported by TRTorch
         </p>
         <p>
          Checks if a method of a TorchScript module can be compiled by TRTorch, if not, a list of operators
that are not supported are printed out and the function returns false, else true.
         </p>
         <dl class="field-list simple">
          <dt class="field-odd">
           Parameters
          </dt>
          <dd class="field-odd">
           <ul class="simple">
            <li>
             <p>
              <strong>
               module
              </strong>
              (
              <em>
               torch.jit.ScriptModule
              </em>
              ) – Source module, a result of tracing or scripting a PyTorch
              <code class="docutils literal notranslate">
               <span class="pre">
                torch.nn.Module
               </span>
              </code>
             </p>
            </li>
            <li>
             <p>
              <strong>
               method_name
              </strong>
              (
              <em>
               str
              </em>
              ) – Name of method to check
             </p>
            </li>
           </ul>
          </dd>
          <dt class="field-even">
           Returns
          </dt>
          <dd class="field-even">
           <p>
            True if supported Method
           </p>
          </dd>
          <dt class="field-odd">
           Return type
          </dt>
          <dd class="field-odd">
           <p>
            bool
           </p>
          </dd>
         </dl>
        </dd>
       </dl>
       <dl class="py function">
        <dt id="trtorch.dump_build_info">
         <code class="sig-prename descclassname">
          trtorch.
         </code>
         <code class="sig-name descname">
          dump_build_info
         </code>
         <span class="sig-paren">
          (
         </span>
         <span class="sig-paren">
          )
         </span>
         <a class="headerlink" href="#trtorch.dump_build_info" title="Permalink to this definition">
          ¶
         </a>
        </dt>
        <dd>
         <p>
          Prints build information about the TRTorch distribution to stdout
         </p>
        </dd>
       </dl>
       <dl class="py function">
        <dt id="trtorch.get_build_info">
         <code class="sig-prename descclassname">
          trtorch.
         </code>
         <code class="sig-name descname">
          get_build_info
         </code>
         <span class="sig-paren">
          (
         </span>
         <span class="sig-paren">
          )
         </span>
         → str
         <a class="headerlink" href="#trtorch.get_build_info" title="Permalink to this definition">
          ¶
         </a>
        </dt>
        <dd>
         <p>
          Returns a string containing the build information of TRTorch distribution
         </p>
         <dl class="field-list simple">
          <dt class="field-odd">
           Returns
          </dt>
          <dd class="field-odd">
           <p>
            String containing the build information for TRTorch distribution
           </p>
          </dd>
          <dt class="field-even">
           Return type
          </dt>
          <dd class="field-even">
           <p>
            str
           </p>
          </dd>
         </dl>
        </dd>
       </dl>
       <h2 id="enums">
        Enums
        <a class="headerlink" href="#enums" title="Permalink to this headline">
         ¶
        </a>
       </h2>
       <dl class="py class">
        <dt id="trtorch.dtype">
         <em class="property">
          class
         </em>
         <code class="sig-prename descclassname">
          trtorch.
         </code>
         <code class="sig-name descname">
          dtype
         </code>
         <a class="headerlink" href="#trtorch.dtype" title="Permalink to this definition">
          ¶
         </a>
        </dt>
        <dd>
         <p>
          Enum to specifiy operating precision for engine execution
         </p>
         <p>
          Members:
         </p>
         <blockquote>
          <div>
           <p>
            float : 32 bit floating point number
           </p>
           <p>
            float32 : 32 bit floating point number
           </p>
           <p>
            half : 16 bit floating point number
           </p>
           <p>
            float16 : 16 bit floating point number
           </p>
           <p>
            int8 : 8 bit integer number
           </p>
          </div>
         </blockquote>
        </dd>
       </dl>
       <dl class="py class">
        <dt id="trtorch.DeviceType">
         <em class="property">
          class
         </em>
         <code class="sig-prename descclassname">
          trtorch.
         </code>
         <code class="sig-name descname">
          DeviceType
         </code>
         <a class="headerlink" href="#trtorch.DeviceType" title="Permalink to this definition">
          ¶
         </a>
        </dt>
        <dd>
         <p>
          Enum to specify device kinds to build TensorRT engines for
         </p>
         <p>
          Members:
         </p>
         <blockquote>
          <div>
           <p>
            gpu : Specify using GPU to execute TensorRT Engine
           </p>
           <p>
            dla : Specify using DLA to execute TensorRT Engine (Jetson Only)
           </p>
          </div>
         </blockquote>
        </dd>
       </dl>
       <dl class="py class">
        <dt id="trtorch.EngineCapability">
         <em class="property">
          class
         </em>
         <code class="sig-prename descclassname">
          trtorch.
         </code>
         <code class="sig-name descname">
          EngineCapability
         </code>
         <a class="headerlink" href="#trtorch.EngineCapability" title="Permalink to this definition">
          ¶
         </a>
        </dt>
        <dd>
         <p>
          Enum to specify engine capability settings (selections of kernels to meet safety requirements)
         </p>
         <p>
          Members:
         </p>
         <blockquote>
          <div>
           <p>
            safe_gpu : Use safety GPU kernels only
           </p>
           <p>
            safe_dla : Use safety DLA kernels only
           </p>
           <p>
            default : Use default behavior
           </p>
          </div>
         </blockquote>
        </dd>
       </dl>
       <h2 id="submodules">
        Submodules
        <a class="headerlink" href="#submodules" title="Permalink to this headline">
         ¶
        </a>
       </h2>
       <div class="toctree-wrapper compound">
        <ul>
         <li class="toctree-l1">
          <a class="reference internal" href="logging.html">
           trtorch.logging
          </a>
         </li>
        </ul>
       </div>
      </article>
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